This invention relates in general to a probability assessment method. Specifically, the invention relates to a method for generating and updating a probability distribution for a desired variable.
It is commonly known that as equipment is used, it sustains wear and tear. If the wear and tear is not monitored, the equipment may fail. This can be particularly catastrophic in the case of vehicles, airplanes, bridges, buildings, etc. Therefore, it is extremely important to closely monitor the structural integrity of such equipment.
Current military approaches to supervision of the structural integrity of their vehicles have successfully avoided catastrophic structural failure. However, this safety has been achieved at the expense of operation readiness and added costs.
Uncertainty about the actual state of structural degradation, and enforced, conservative maintenance schedules result in non-optimum vehicle availability. For instance, within a given aircraft fleet, several aircraft may be grounded, while others may be restricted to limited flight profiles because they have reached or surpassed 100% of their calculated life. A common approach employed by the U.S. Navy called “safe-life,” uses a cumulative damage index known as Fatigue Life Expended (FLE). When FLE reaches 100%, the probability of having an initiated crack of 0.01″ or greater is approximately 0.001. Thus, given 1000 aircraft all having their FLE=100%, only one of them is likely to have a crack of 0.01″ or greater. At 100% FLE, some aircraft have actually accumulated only 50% of the damage required to form a 250 μm (0.01-in.) crack at their “hot spot.” The “hot spot” is the most fatigue critical location in the airframe. The location of the hot spot and the service life required for crack formation are determined by a full-scale fatigue test. In many cases, the full-scale fatigue test was performed many years ago. A safety factor of two is generally used because of the combined uncertainties in many factors, including: the microstructurally-based stochastic nature of the damage accumulation process; differences between the original test spectrum and the actual flight spectra; uncertainties in the predictive technology; errors in the fatigue tracking algorithms; etc. If the entire fleet were grounded at 100% FLE, a significant portion of the total potential life to the fleet would have been lost. This is a severe financial burden, and also imposes significant limitations on the use of existing aircraft. Therefore, a more accurate failure prediction method would allow for more efficient aircraft usage.
Most of the current failure prediction methods are deterministic (i.e. failure will happen at 2:00 o'clock). Deterministic predictions typically identify the median. In other words, half of the time, the failure has already occurred. As a given prediction becomes more precise, the probability that the given prediction will become true decreases.
The governing parameters for most failure prediction methods are static, meaning that the parameters are generally determined analytically through experimentation. Current methods that dynamically adjust predictions only adjust the current state. The underlying parameters are not adjusted. No existing methods iteratively integrate all available information while accounting for all the associated uncertainty.
As such, there exists a need in the art for providing a prompt, informed prediction method regarding the structural viability of individual aircraft based on tracking their actual use and modeling of anticipated usage.